Tootfinder

testing out the @… Custom Javascript this morning and now I'm down a rabbit hole of trying to understand what Embedding numbers are when extracted from page content. You are too? Here is a great piece from @…

All-optical scanning vector magnetometry based on fine and hyperfine interactions in spin-$\frac{3}{2}$ centers in silicon carbide
Kirill V. Likhachev, Maxim V. Uchaev, Igor P. Veyshtort, Anastasia V. Batueva, Aleksandr S. Gurin, Roman A. Babunts, Pavel G. Baranov
https://arxiv.org/abs/2404.07080…

All-optical scanning vector magnetometry based on fine and hyperfine interactions in spin-$\frac{3}{2}$ centers in silicon carbide
The possibility of using axial spin color centers with $S=3/2$, oriented along the hexagonal $c$ axis in a silicon carbide (SiC) wafer, has been demonstrated for all-optical measurement of projection of the external magnetic field coinciding with the $c$ axis of the crystal, and the polar and azimuthal angles of the external measured magnetic field at room and significantly higher temperatures. A distinctive feature of spin centers in SiC, in which optically induced spin alignment is carried ou…

This https://arxiv.org/abs/2404.00066 has been replaced.
link: https://scholar.google.com/scholar?q=a

Local Observability of VINS and LINS
This work analyzes unobservable directions of Vision-aided Inertial Navigation System (VINS) and Lidar-aided Inertial Navigation System (LINS) nonlinear model. Under the assumption that there exist two features observed by the camera without occlusion, the unobservable directions of VINS are uniformly globally translation and global rotations about the gravity vector. The unobservable directions of LINS are same as VINS, while only one feature need to be observed. Also, a constraint in Observab…

Vector Quantization for Recommender Systems: A Review and Outlook
Qijiong Liu, Xiaoyu Dong, Jiaren Xiao, Nuo Chen, Hengchang Hu, Jieming Zhu, Chenxu Zhu, Tetsuya Sakai, Xiao-Ming Wu
https://arxiv.org/abs/2405.03110

Vector Quantization for Recommender Systems: A Review and Outlook
Vector quantization, renowned for its unparalleled feature compression capabilities, has been a prominent topic in signal processing and machine learning research for several decades and remains widely utilized today. With the emergence of large models and generative AI, vector quantization has gained popularity in recommender systems, establishing itself as a preferred solution. This paper starts with a comprehensive review of vector quantization techniques. It then explores systematic taxonom…

Matter in Discrete Space-Times
P. P. Divakaran
https://arxiv.org/abs/2404.04548 https://arxiv.org/pdf/2404.04548

Matter in Discrete Space-Times
The unitary representations of the Poincare group of a discrete space-time are constructed, following the Wigner method in continuum relativity. They can be interpreted as elementary particles with one significant new feature: the momentum space being the 4-torus is identified as the Brillouin zone of space-time where all physical phenomena occur. Consequently 4-momentum is defined and conserved only modulo a reciprocal lattice vector of the order of the Planck mass, implying that there is no n…

Ultralight vector dark matter search using data from the KAGRA O3GK run
The LIGO Scientific Collaboration, et al.
https://arxiv.org/abs/2403.03004 https://…

Ultralight vector dark matter search using data from the KAGRA O3GK run
Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for $U(1)_{B-L}$ gauge boson DM using the KAGRA data from auxiliary lengt…

This https://arxiv.org/abs/2312.14889 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

On Rate-Optimal Partitioning Classification from Observable and from Privatised Data
In this paper we revisit the classical method of partitioning classification and study its convergence rate under relaxed conditions, both for observable (non-privatised) and for privatised data. Let the feature vector $X$ take values in $\mathbb{R}^d$ and denote its label by $Y$. Previous results on the partitioning classifier worked with the strong density assumption, which is restrictive, as we demonstrate through simple examples. We assume that the distribution of $X$ is a mixture of an abs…

ESC: Efficient Speech Coding with Cross-Scale Residual Vector Quantized Transformers
Yuzhe Gu, Enmao Diao
https://arxiv.org/abs/2404.19441 https://<…

ESC: Efficient Speech Coding with Cross-Scale Residual Vector Quantized Transformers
Existing neural audio codecs usually sacrifice computational complexity for audio quality. They build the feature transformation layers mainly on convolutional blocks, which are not inherently appropriate for capturing local redundancies of audio signals. As compensation, either adversarial losses from a discriminator or a large number of model parameters are required to improve the codec. To that end, we propose Efficient Speech Codec (ESC), a lightweight parameter-efficient codec laid on cros…

Associative Memories in the Feature Space
Tommaso Salvatori, Beren Millidge, Yuhang Song, Rafal Bogacz, Thomas Lukasiewicz
https://arxiv.org/abs/2402.10814

Associative Memories in the Feature Space
An autoassociative memory model is a function that, given a set of data points, takes as input an arbitrary vector and outputs the most similar data point from the memorized set. However, popular memory models fail to retrieve images even when the corruption is mild and easy to detect for a human evaluator. This is because similarities are evaluated in the raw pixel space, which does not contain any semantic information about the images. This problem can be easily solved by computing \emph{simi…

Feature selection in linear SVMs via hard cardinality constraint: a scalable SDP decomposition approach
Immanuel Bomze, Federico D'Onofrio, Laura Palagi, Bo Peng
https://arxiv.org/abs/2404.10099

Feature selection in linear SVMs via hard cardinality constraint: a scalable SDP decomposition approach
In this paper, we study the embedded feature selection problem in linear Support Vector Machines (SVMs), in which a cardinality constraint is employed, leading to a fully explainable selection model. The problem is NP-hard due to the presence of the cardinality constraint, even though the original linear SVM amounts to a problem solvable in polynomial time. To handle the hard problem, we first introduce two mixed-integer formulations for which novel SDP relaxations are proposed. Exploiting the …

A Port-Hamiltonian System Perspective on Electromagneto-Quasistatic Field Formulations of Darwin-Type
Markus Clemens, Marvin-Lucas Henkel, Fotios Kasolis, Michael G\"unther
https://arxiv.org/abs/2404.18767

A Port-Hamiltonian System Perspective on Electromagneto-Quasistatic Field Formulations of Darwin-Type
Electromagneto-quasistatic (EMQS) field formulations are often dubbed as Darwin-type field formulations which approximate the Maxwell equations by neglecting radiation effects while modelling resistive, capacitive, and inductive effects. A common feature of EMQS field models is the Darwin-Ampére equation formulated with the magnetic vector potential and the electric scalar potential. EMQS field formulations yield different approximations to the Maxwell equations by choice of additional gauge e…

UDON: A case for offloading to general purpose compute on CXL memory
Jon Hermes, Josh Minor, Minjun Wu, Adarsh Patil, Eric Van Hensbergen
https://arxiv.org/abs/2404.02868

UDON: A case for offloading to general purpose compute on CXL memory
Upcoming CXL-based disaggregated memory devices feature special purpose units to offload compute to near-memory. In this paper, we explore opportunities for offloading compute to general purpose cores on CXL memory devices, thereby enabling a greater utility and diversity of offload. We study two classes of popular memory intensive applications: ML inference and vector database as candidates for computational offload. The study uses Arm AArch64-based dual-socket NUMA systems to emulate CXL ty…

Lower Limb Movements Recognition Based on Feature Recursive Elimination and Backpropagation Neural Network
Yongkai Ma, Shili Liang, Zekun Chen
https://arxiv.org/abs/2404.11383

Lower Limb Movements Recognition Based on Feature Recursive Elimination and Backpropagation Neural Network
Surface electromyographic (sEMG) signal serve as a signal source commonly used for lower limb movement recognition, reflecting the intent of human movement. However, it has been a challenge to improve the movements recognition rate while using fewer features in this area of research area. In this paper, a method for lower limb movements recognition based on recursive feature elimination and backpropagation neural network of support vector machine is proposed. First, the sEMG signal of five subj…

This https://arxiv.org/abs/2305.00979 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Spectral clustering in the Gaussian mixture block model
Gaussian mixture block models are distributions over graphs that strive to model modern networks: to generate a graph from such a model, we associate each vertex $i$ with a latent feature vector $u_i \in \mathbb{R}^d$ sampled from a mixture of Gaussians, and we add edge $(i,j)$ if and only if the feature vectors are sufficiently similar, in that $\langle u_i,u_j \rangle \ge τ$ for a pre-specified threshold $τ$. The different components of the Gaussian mixture represent the fact that there may…

A Port-Hamiltonian System Perspective on Electromagneto-Quasistatic Field Formulations of Darwin-Type
Markus Clemens, Marvin-Lucas Henkel, Fotios Kasolis, Michael G\"unther
https://arxiv.org/abs/2404.18767

A Port-Hamiltonian System Perspective on Electromagneto-Quasistatic Field Formulations of Darwin-Type
Electromagneto-quasistatic (EMQS) field formulations are often dubbed as Darwin-type field formulations which approximate the Maxwell equations by neglecting radiation effects while modelling resistive, capacitive, and inductive effects. A common feature of EMQS field models is the Darwin-Ampére equation formulated with the magnetic vector potential and the electric scalar potential. EMQS field formulations yield different approximations to the Maxwell equations by choice of additional gauge e…

Nonlinear kernel-free quadratic hyper-surface support vector machine with 0-1 loss function
Mingyang Wu, Zhixia Yang, Junyou Ye
https://arxiv.org/abs/2404.10559

Nonlinear kernel-free quadratic hyper-surface support vector machine with 0-1 loss function
For the binary classification problem, a novel nonlinear kernel-free quadratic hyper-surface support vector machine with 0-1 loss function (QSSVM$_{0/1}$) is proposed. Specifically, the task of QSSVM$_{0/1}$ is to seek a quadratic separating hyper-surface to divide the samples into two categories. And it has better interpretability than the methods using kernel functions, since each feature of the sample acts both independently and synergistically. By introducing the 0-1 loss function to constr…

Performance of using Mel-Frequency Cepstrum Based Features in Nonlinear Classifiers for Phonocardiography Recordings
Ibrahim Ozkan, Atila Yilmaz
https://arxiv.org/abs/2402.12540 <…

Performance of using Mel-Frequency Cepstrum Based Features in Nonlinear Classifiers for Phonocardiography Recordings
Cardiovascular system diseases can be identified by using a specialized diagnostic process utilizing a digital stethoscope. Digital stethoscopes provide phonocardiography (PCG) recordings for further inspection, besides filtering and amplification of heart sounds. In this paper, a framework that is useful to develop feature extraction and classification of PCG recordings is presented. This framework is built upon a previously proposed segmentation algorithm that processes a feature vector produ…

Theory of paraxial optical Skyrmions
Z. Ye, S. M. Barnett, S. Franke-Arnold, J. B. G\"otte, A. McWilliam, F. C. Speirits, C. M. Cisowski
https://arxiv.org/abs/2404.11530

Theory of paraxial optical Skyrmions
Vector light beams, characterised by a spatially varying polarisation, can exhibit localised structures reminiscent of the Skyrmions familiar from the study of magnetic media. We present a theory of such Skyrmions within paraxial optics, exploiting mathematical analogies with the study of superfluids, especially the A phase of superfluid $\textrm{He}^3$. The key feature is the Skyrmion field which, together with the underlying Skyrmion vector potential, determines the properties of the Skyrmion…

A robust SVM-based approach with feature selection and outliers detection for classification problems
Marta Baldomero-Naranjo, Luisa I. Mart\'inez-Merino, Antonio M. Rodr\'iguez-Ch\'ia
https://arxiv.org/abs/2403.07753

A robust SVM-based approach with feature selection and outliers detection for classification problems
This paper proposes a robust classification model, based on support vector machine (SVM), which simultaneously deals with outliers detection and feature selection. The classifier is built considering the ramp loss margin error and it includes a budget constraint to limit the number of selected features. The search of this classifier is modeled using a mixed-integer formulation with big M parameters. Two different approaches (exact and heuristic) are proposed to solve the model. The heuristic ap…

This https://arxiv.org/abs/2403.07846 has been replaced.
initial toot: https://mastoxiv.page/@a…

Antiferromagnetic magnons on a Möbius strip: topology-induced symmetry breaking
A Möbius strip comprising of two antiferromagnetically coupled spin chains showcase exotic magnon excitations with non-trivial physical behavior, which is solely attributed to the real-space boundary condition even in the absence of local curvature effects. Counterintuitively, magnons on a Möbius strip feature linear polarization of the Néel vector devoid of chirality even when the spin Hamiltonian preserves local rotational symmetry around the anisotropic easy axis. These linearly-polarized…

Brain-grounding of semantic vectors improves neural decoding of visual stimuli
Shirin Vafaei, Ryohei Fukuma, Huixiang Yang, Haruhiko Kishima, Takufumi Yanagisawa
https://arxiv.org/abs/2403.15176

Brain-grounding of semantic vectors improves neural decoding of visual stimuli
Developing algorithms for accurate and comprehensive neural decoding of mental contents is one of the long-cherished goals in the field of neuroscience and brain-machine interfaces. Previous studies have demonstrated the feasibility of neural decoding by training machine learning models to map brain activity patterns into a semantic vector representation of stimuli. These vectors, hereafter referred as pretrained feature vectors, are usually derived from semantic spaces based solely on image an…

Performance of using Mel-Frequency Cepstrum Based Features in Nonlinear Classifiers for Phonocardiography Recordings
Ibrahim Ozkan, Atila Yilmaz
https://arxiv.org/abs/2402.12540 <…

Performance of using Mel-Frequency Cepstrum Based Features in Nonlinear Classifiers for Phonocardiography Recordings
Cardiovascular system diseases can be identified by using a specialized diagnostic process utilizing a digital stethoscope. Digital stethoscopes provide phonocardiography (PCG) recordings for further inspection, besides filtering and amplification of heart sounds. In this paper, a framework that is useful to develop feature extraction and classification of PCG recordings is presented. This framework is built upon a previously proposed segmentation algorithm that processes a feature vector produ…

This https://arxiv.org/abs/2305.00979 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Spectral clustering in the Gaussian mixture block model
Gaussian mixture block models are distributions over graphs that strive to model modern networks: to generate a graph from such a model, we associate each vertex $i$ with a latent feature vector $u_i \in \mathbb{R}^d$ sampled from a mixture of Gaussians, and we add edge $(i,j)$ if and only if the feature vectors are sufficiently similar, in that $\langle u_i,u_j \rangle \ge τ$ for a pre-specified threshold $τ$. The different components of the Gaussian mixture represent the fact that there may…

A robust SVM-based approach with feature selection and outliers detection for classification problems
Marta Baldomero-Naranjo, Luisa I. Mart\'inez-Merino, Antonio M. Rodr\'iguez-Ch\'ia
https://arxiv.org/abs/2403.07753

A robust SVM-based approach with feature selection and outliers detection for classification problems
This paper proposes a robust classification model, based on support vector machine (SVM), which simultaneously deals with outliers detection and feature selection. The classifier is built considering the ramp loss margin error and it includes a budget constraint to limit the number of selected features. The search of this classifier is modeled using a mixed-integer formulation with big M parameters. Two different approaches (exact and heuristic) are proposed to solve the model. The heuristic ap…

This https://arxiv.org/abs/2404.00066 has been replaced.
link: https://scholar.google.com/scholar?q=a

Local Observability of VINS and LINS
This work analyzes unobservable directions of Vision-aided Inertial Navigation System (VINS) and Lidar-aided Inertial Navigation System (LINS) nonlinear model. Under the assumption that there exist two features observed by the camera without occlusion, the unobservable directions of VINS are uniformly globally translation and global rotations about the gravity vector. The unobservable directions of LINS are same as VINS, while only one feature need to be observed. Also, a constraint in Observab…

Brain-grounding of semantic vectors improves neural decoding of visual stimuli
Shirin Vafaei, Ryohei Fukuma, Huixiang Yang, Haruhiko Kishima, Takufumi Yanagisawa
https://arxiv.org/abs/2403.15176

Brain-grounding of semantic vectors improves neural decoding of visual stimuli
Developing algorithms for accurate and comprehensive neural decoding of mental contents is one of the long-cherished goals in the field of neuroscience and brain-machine interfaces. Previous studies have demonstrated the feasibility of neural decoding by training machine learning models to map brain activity patterns into a semantic vector representation of stimuli. These vectors, hereafter referred as pretrained feature vectors, are usually derived from semantic spaces based solely on image an…

Antiferromagnetic magnons on a M\"obius strip: topology-induced symmetry breaking
Kuangyin Deng, Ran Cheng
https://arxiv.org/abs/2403.07846 https://…

Antiferromagnetic magnons on a Möbius strip: topology-induced symmetry breaking
We study a Möbius strip comprising of two antiferromagnetically coupled spin chains. To satisfy the boundary condition, magnon excitations feature linear polarization of the Néel vector devoid of chirality, forming two non-degenerate branches of modes that can neither be smoothly connected to nor be decomposed by the circularly-polarized magnons of opposite chirality commonly found in antiferromagnets. Only one branch supports standing-wave formation on the Möbius strip while the other does …

Cepstral Analysis Based Artifact Detection, Recognition and Removal for Prefrontal EEG
Siqi Han, Chao Zhang, Jiaxin Lei, Qingquan Han, Yuhui Du, Anhe Wang, Shuo Bai, Milin Zhang
https://arxiv.org/abs/2404.08199

Cepstral Analysis Based Artifact Detection, Recognition and Removal for Prefrontal EEG
This paper proposes to use cepstrum for artifact detection, recognition and removal in prefrontal EEG. This work focuses on the artifact caused by eye movement. A database containing artifact-free EEG and eye movement contaminated EEG from different subjects is established. A cepstral analysis-based feature extraction with support vector machine (SVM) based classifier is designed to identify the artifacts from the target EEG signals. The proposed method achieves an accuracy of 99.62% on the art…

This https://arxiv.org/abs/2401.11886 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Time Localized Tilted Beams in Nearly-Degenerate Laser Cavities
We show that nearly degenerate Vertical External-Cavity Surface-Emitting Lasers emit tilted beams of time localized structures, i.e. mode-locked light pulses which can be individually addressed. These beams feature a Gaussian profile and they are emitted in pairs with opposite transverse k-vector. Because they are phase locked, their interference leads to a non homotetic pattern in the near-field emission of the laser. When a single pair is emitted this is a stripe pattern. Our analysis disclos…

Cepstral Analysis Based Artifact Detection, Recognition and Removal for Prefrontal EEG
Siqi Han, Chao Zhang, Jiaxin Lei, Qingquan Han, Yuhui Du, Anhe Wang, Shuo Bai, Milin Zhang
https://arxiv.org/abs/2404.08199

Cepstral Analysis Based Artifact Detection, Recognition and Removal for Prefrontal EEG
This paper proposes to use cepstrum for artifact detection, recognition and removal in prefrontal EEG. This work focuses on the artifact caused by eye movement. A database containing artifact-free EEG and eye movement contaminated EEG from different subjects is established. A cepstral analysis-based feature extraction with support vector machine (SVM) based classifier is designed to identify the artifacts from the target EEG signals. The proposed method achieves an accuracy of 99.62% on the art…